Applied Unsupervised Learning in Python
Completed by El Hadji Faly Seck
July 15, 2026
30 hours (approximately)
El Hadji Faly Seck's account is verified. Coursera certifies their successful completion of Applied Unsupervised Learning in Python
What you will learn
Apply unsupervised learning methods, such as dimensionality reduction, manifold learning, and density estimation, to transform and visualize data.
Understand, evaluate, optimize, and correctly apply clustering algorithms using hierarchical, partitioning, and density-based methods.
Use topic modeling to find important themes in text data and use word embeddings to analyze patterns in text data.
Manage missing data using supervised and unsupervised imputation methods, and use semi-supervised learning to work with partially-labeled datasets.
Skills you will gain
- Category: Model Evaluation
- Category: Embeddings
- Category: Data Transformation
- Category: Anomaly Detection
- Category: Unstructured Data
- Category: Machine Learning Methods
- Category: Unsupervised Learning
- Category: Data Preprocessing
- Category: Exploratory Data Analysis
- Category: Applied Machine Learning
- Category: Data Quality
- Category: Python Programming
